The NMT predictor is a neural machine translation model based on transformer architecture, that automatically transforms sentences in the source language to their translations in the target language.
For the purpose of sufficiently exploiting the symmetric structure of machine translation, we propose to adopt model-level dual learning on the NMT predictor, and train it on both directions of translation.
Additionally, we apply the method of mixture models [1]} for diverse machine translation during the training of the NMT predictor, in order to increase the diversity of the translation outputs and derive more robust features for evaluation.
